{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T21:14:58Z","timestamp":1781298898868,"version":"3.54.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T00:00:00Z","timestamp":1682380800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T00:00:00Z","timestamp":1682380800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Swedish Research Council (VR)."},{"DOI":"10.13039\/501100009244","name":"Stockholm University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100009244","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Tech Know Learn"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Self-regulated learning is an essential skill that can help students plan, monitor, and reflect on their learning in order to achieve their learning goals. However, in situations where there is a lack of effective feedback and recommendations, it becomes challenging for students to self-regulate their learning. In this paper, we propose an explainable AI-based approach to provide automatic and intelligent feedback and recommendations that can support the self-regulation of students\u2019 learning in a data-driven manner, with the aim of improving their performance on their courses. Prior studies have predicted students\u2019 performance and have used these predicted outcomes as feedback, without explaining the reasons behind the predictions. Our proposed approach is based on an algorithm that explains the root causes behind a decline in student performance, and generates data-driven recommendations for taking appropriate actions. The proposed approach was implemented in the form of a dashboard to support self-regulation by students on a university course, and was evaluated to determine its effects on the students\u2019 academic performance. The results revealed that the dashboard significantly enhanced students\u2019 learning achievements and improved their self-regulated learning skills. Furthermore, it was found that the recommendations generated by the proposed approach positively affected students\u2019 performance and assisted them in self-regulation<\/jats:p>","DOI":"10.1007\/s10758-023-09650-0","type":"journal-article","created":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T11:15:01Z","timestamp":1682421301000},"page":"331-354","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":77,"title":["Informative Feedback and Explainable AI-Based Recommendations to Support Students\u2019 Self-regulation"],"prefix":"10.1007","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2054-0971","authenticated-orcid":false,"given":"Muhammad","family":"Afzaal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aayesha","family":"Zia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jalal","family":"Nouri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Uno","family":"Fors","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,4,25]]},"reference":[{"key":"9650_CR1","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (Xai). IEEE Access, 6, 52138\u201352160.","journal-title":"IEEE Access"},{"key":"9650_CR2","doi-asserted-by":"crossref","unstructured":"Afzaal, M., Nouri, J., Zia, A., Papapetrou, P., Fors, U., Wu, Y., Li, X., & Weegar, R. (2021). Explainable ai for data-driven feedback and intelligent action recommendations to support students self-regulation. Frontiers in Artificial Intelligence, 4.","DOI":"10.3389\/frai.2021.723447"},{"issue":"1","key":"9650_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41239-019-0172-z","volume":"16","author":"G Ak\u00e7ap\u0131nar","year":"2019","unstructured":"Ak\u00e7ap\u0131nar, G., Altun, A., & A\u015fkar, P. (2019). Using learning analytics to develop early-warning system for at-risk students. International Journal of Educational Technology in Higher Education, 16(1), 1\u201320.","journal-title":"International Journal of Educational Technology in Higher Education"},{"issue":"5","key":"9650_CR4","doi-asserted-by":"publisher","first-page":"397","DOI":"10.34190\/JEL.18.5.003","volume":"18","author":"MG Algayres","year":"2020","unstructured":"Algayres, M. G., & Triantafyllou, E. (2020). Learning analytics in flipped classrooms: A scoping review. Electronic Journal of e-Learning, 18(5), 397\u2013409.","journal-title":"Electronic Journal of e-Learning"},{"issue":"3","key":"9650_CR5","doi-asserted-by":"publisher","first-page":"56","DOI":"10.14742\/ajet.6150","volume":"37","author":"ME Alonso-Menc\u00eda","year":"2021","unstructured":"Alonso-Menc\u00eda, M. E., Alario-Hoyos, C., Est\u00e9vez-Ayres, I., & Kloos, C. D. (2021). Analysing self-regulated learning strategies of Mooc learners through self-reported data. Australasian Journal of Educational Technology, 37(3), 56\u201370.","journal-title":"Australasian Journal of Educational Technology"},{"issue":"2","key":"9650_CR6","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1109\/TLT.2019.2912167","volume":"12","author":"D Baneres","year":"2019","unstructured":"Baneres, D., Rodr\u00edguez-Gonzalez, M. E., & Serra, M. (2019). An early feedback prediction system for learners at-risk within a first-year higher education course. IEEE Transactions on Learning Technologies, 12(2), 249\u2013263.","journal-title":"IEEE Transactions on Learning Technologies"},{"issue":"2","key":"9650_CR7","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1109\/TLT.2019.2911079","volume":"12","author":"A Cano","year":"2019","unstructured":"Cano, A., & Leonard, J. D. (2019). Interpretable multiview early warning system adapted to underrepresented student populations. IEEE Transactions on Learning Technologies, 12(2), 198\u2013211.","journal-title":"IEEE Transactions on Learning Technologies"},{"issue":"1","key":"9650_CR8","doi-asserted-by":"publisher","first-page":"173","DOI":"10.19173\/irrodl.v21i1.4557","volume":"21","author":"T Cavanagh","year":"2020","unstructured":"Cavanagh, T., Chen, B., Lahcen, R. A. M., & Paradiso, J. R. (2020). Constructing a design framework and pedagogical approach for adaptive learning in higher education: A practitioner\u2019s perspective. International Review of Research in Open and Distributed Learning, 21(1), 173\u2013197.","journal-title":"International Review of Research in Open and Distributed Learning"},{"key":"9650_CR9","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). Smote: Synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321\u2013357.","journal-title":"Journal of artificial intelligence research"},{"key":"9650_CR10","doi-asserted-by":"crossref","unstructured":"Cicchinelli, A., Veas, E., Pardo, A., Pammer-Schindler, V., Fessl, A., Barreiros, C., & Lindst\u00e4dt, S. (2018). Finding traces of self-regulated learning in activity streams. In: In Proceedings of the 8th international conference on learning analytics and knowledge. pp. 191-200.","DOI":"10.1145\/3170358.3170381"},{"key":"9650_CR11","unstructured":"Corrin, L., & De Barba, P. (2014). Exploring students\u2019 interpretation of feedback delivered through learning analytics dashboards [pp. 629-633]. B. Hegarty, J. McDonald y S.-K. Loke, Rhetoric and Reality: Critical perspectives on educational technology. Dunedin: University of Melbourne. Recuperado de https:\/\/melbourne-cshe.unimelb.edu.au\/research\/research-projects\/edutech\/learninganalytics-dashboards."},{"key":"9650_CR12","unstructured":"Davis, D., Chen, G., Jivet, I., Hauff, C., & Houben, G.-J. (2016). Encouraging metacognition & self-regulation in Moocs through increased learner feedback. LAL@ LAK, pp. 17-22."},{"issue":"2","key":"9650_CR13","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1002\/job.1962","volume":"36","author":"JA DeSimone","year":"2015","unstructured":"DeSimone, J. A., Harms, P. D., & DeSimone, A. J. (2015). Best practice recommendations for data screening. Journal of Organizational Behavior, 36(2), 171\u2013181.","journal-title":"Journal of Organizational Behavior"},{"key":"9650_CR14","doi-asserted-by":"crossref","unstructured":"Di Mitri, D., Scheffel, M., Drachsler, H., B\u00f6rner, D., Ternier, S., & Specht, M. (2017). Learning pulse: A machine learning approach for predicting performance in self-regulated learning using multimodal data. In: Proceedings of the seventh international learning analytics & knowledge conference, pp. 188- 197.","DOI":"10.1145\/3027385.3027447"},{"key":"9650_CR15","doi-asserted-by":"crossref","unstructured":"Fariani, R. I., Junus, K., & Santoso, H. B. (2022). A systematic literature review on personalised learning in the higher education context (pp. 1\u201328). Knowledge and learning: Technology.","DOI":"10.1007\/s10758-022-09628-4"},{"key":"9650_CR16","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.compedu.2018.05.006","volume":"123","author":"R Garcia","year":"2018","unstructured":"Garcia, R., Falkner, K., & Vivian, R. (2018). Systematic literature review: Self-regulated learning strategies using e-learning tools for computer science. Computers & Education, 123, 150\u2013163.","journal-title":"Computers & Education"},{"key":"9650_CR17","doi-asserted-by":"crossref","unstructured":"Ga\u0161evi\u0107, D., Mirriahi, N., & Dawson, S. (2014). Analytics of the effects of video use and instruction to support reflective learning. In: Proceedings of the fourth international conference on learning analytics and Knowledge, pp. 123-132.","DOI":"10.1145\/2567574.2567590"},{"key":"9650_CR18","doi-asserted-by":"crossref","unstructured":"Kizilcec, R. F., P\u00e9rez-Sanagust\u00edn, M., & Maldonado, J. J. (2016). Recommending self-regulated learning strategies does not improve performance in a Mooc. In: Proceedings of the third (2016) ACM conference on learning@ scale, pp. 101-104.","DOI":"10.1145\/2876034.2893378"},{"key":"9650_CR19","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.compedu.2016.10.001","volume":"104","author":"RF Kizilcec","year":"2017","unstructured":"Kizilcec, R. F., P\u00e9rez-Sanagust\u00edn, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in massive open online courses. Computers & Education, 104, 18\u201333.","journal-title":"Computers & Education"},{"key":"9650_CR20","doi-asserted-by":"crossref","unstructured":"Lau, C., Sinclair, J., Taub, M., Azevedo, R., & Jang, E. E. (2017). Transitioning self-regulated learning profiles in hypermedia-learning environments. In: Proceedings of the seventh international learning analytics & knowledge conference, pp. 198-202.","DOI":"10.1145\/3027385.3027443"},{"key":"9650_CR21","doi-asserted-by":"publisher","first-page":"42467","DOI":"10.1109\/ACCESS.2018.2860519","volume":"6","author":"M Manso-V\u00e1zquez","year":"2018","unstructured":"Manso-V\u00e1zquez, M., Caeiro-Rodr\u00edguez, M., & Llamas-Nistal, M. (2018). An Xapi application profile to monitor self-regulated learning strategies. IEEE Access, 6, 42467\u201342481.","journal-title":"IEEE Access"},{"issue":"2","key":"9650_CR22","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1109\/TLT.2019.2916802","volume":"13","author":"W Matcha","year":"2019","unstructured":"Matcha, W., Ga\u0161evi\u0107, D., & Pardo, A. (2019). A systematic review of empirical studies on learning analytics dashboards: A self-regulated learning perspective. IEEE Transactions on Learning Technologies, 13(2), 226\u2013245.","journal-title":"IEEE Transactions on Learning Technologies"},{"key":"9650_CR23","doi-asserted-by":"crossref","unstructured":"Matcha, W., Ga\u0161evi\u0107, D., Uzir, N. A., Jovanovi\u0107, J., & Pardo, A. (2019). Analytics of learning strategies: Associations with academic performance and feedback. In: Proceedings of the 9th international conference on learning analytics & knowledge, pp. 461-470.","DOI":"10.1145\/3303772.3303787"},{"key":"9650_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.learninstruc.2019.05.003","volume":"72","author":"I Molenaar","year":"2021","unstructured":"Molenaar, I., Horvers, A., & Baker, R. S. (2021). What can moment-by-moment learning curves tell about students\u2019 self-regulated learning? Learning and Instruction, 72, 101206.","journal-title":"Learning and Instruction"},{"issue":"1","key":"9650_CR25","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1111\/bjet.12590","volume":"50","author":"AP Montgomery","year":"2019","unstructured":"Montgomery, A. P., Mousavi, A., Carbonaro, M., Hayward, D. V., & Dunn, W. (2019). Using learning analytics to explore self-regulated learning in flipped blended learning music teacher education. British Journal of Educational Technology, 50(1), 114\u2013127.","journal-title":"British Journal of Educational Technology"},{"key":"9650_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2019.103728","volume":"145","author":"PM Moreno-Marcos","year":"2020","unstructured":"Moreno-Marcos, P. M., Mu\u00f1oz-Merino, P. J., Maldonado-Mahauad, J., Perez-Sanagustin, M., Alario- Hoyos, C., & Kloos, C. D. (2020). Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced Moocs. Computers & Education, 145, 103728.","journal-title":"Computers & Education"},{"key":"9650_CR27","doi-asserted-by":"crossref","unstructured":"Mothilal, R. K., Sharma, A., & Tan, C. (2020). Explaining machine learning classifiers through diverse counterfactual explanations. in: Proceedings of the 2020 conference on fairness, accountability, and transparency, pp. 607-617.","DOI":"10.1145\/3351095.3372850"},{"issue":"8","key":"9650_CR28","doi-asserted-by":"publisher","first-page":"1414","DOI":"10.1080\/10494820.2020.1727529","volume":"30","author":"AA Mubarak","year":"2022","unstructured":"Mubarak, A. A., Cao, H., & Zhang, W. (2022). Prediction of students\u2019 early dropout based on their interaction logs in online learning environment. Interactive Learning Environments, 30(8), 1414\u20131433.","journal-title":"Interactive Learning Environments"},{"key":"9650_CR29","unstructured":"Nouri, J., Saqr, M., & Fors, U. (2019). Predicting performance of students in a flipped classroom using machine learning: Towards automated data-driven formative feedback. In: 10th international conference on education, training and informatics (ICETI 2019), vol 17, pp. 17-21."},{"key":"9650_CR30","doi-asserted-by":"crossref","unstructured":"O\u2019Lynn, R. G. (2021). Designing effective feedback processes in higher education: A learning-focused approach (research into higher education). The Wabash Center Journal on Teaching, 2 (2).","DOI":"10.31046\/wabashcenter.v2i2.2910"},{"issue":"2","key":"9650_CR31","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1080\/08993408.2015.1033129","volume":"25","author":"C Ott","year":"2015","unstructured":"Ott, C., Robins, A., Haden, P., & Shephard, K. (2015). Illustrating performance indicators and course characteristics to support students\u2019 self-regulated learning in cs1. Computer Science Education, 25(2), 174\u2013198.","journal-title":"Computer Science Education"},{"key":"9650_CR32","doi-asserted-by":"crossref","unstructured":"Papamitsiou, Z., Economides, A. A., Pappas, I. O., & Giannakos, M. N. (2018). Explaining learning performance using response-time, self-regulation and satisfaction from content: An FSQCA approach. In: Proceedings of the 8th international conference on learning analytics and knowledge, pp. 181-190.","DOI":"10.1145\/3170358.3170397"},{"key":"9650_CR33","doi-asserted-by":"crossref","unstructured":"Ramaswami, G., Susnjak, T., Mathrani, A., & Umer, R. (2022). Use of predictive analytics within learning analytics dashboards: A review of case studies (pp. 1\u201322). Knowledge and learning: Technology.","DOI":"10.1007\/s10758-022-09613-x"},{"key":"9650_CR34","doi-asserted-by":"crossref","unstructured":"Rohloff, T., & Meinel, C. (2018). Towards personalized learning objectives in Moocs. In: European conference on technology enhanced learning, pp. 202-215.","DOI":"10.1007\/978-3-319-98572-5_16"},{"key":"9650_CR35","doi-asserted-by":"publisher","first-page":"745","DOI":"10.3389\/fpsyg.2017.00745","volume":"8","author":"MC S\u00e1iz Manzanares","year":"2017","unstructured":"S\u00e1iz Manzanares, M. C., Marticorena S\u00e1nchez, R., Garc\u00eda Osorio, C. I., & D\u00edez-Pastor, J. F. (2017). How do b-learning and learning patterns influence learning outcomes? Frontiers in Psychology, 8, 745.","journal-title":"Frontiers in Psychology"},{"key":"9650_CR36","doi-asserted-by":"crossref","unstructured":"Scheffel, M., Kirschenmann, U., Taske, A., Adloff, K., Kiesel, M., Klemke, R., & Wolpers, M. (2013). Exploring logiassist-the mobile learning and assistance platform for truck drivers. In: European conference on technology enhanced learning, pp. 343-356.","DOI":"10.1007\/978-3-642-40814-4_27"},{"key":"9650_CR37","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.chb.2016.02.025","volume":"59","author":"M Siadaty","year":"2016","unstructured":"Siadaty, M., Ga\u0161evi\u0107, D., & Hatala, M. (2016). Measuring the impact of technological scaffolding interventions on micro-level processes of self-regulated workplace learning. Computers in Human Behavior, 59, 469\u2013482.","journal-title":"Computers in Human Behavior"},{"key":"9650_CR38","doi-asserted-by":"crossref","unstructured":"Silva, J. C. S., Zambom, E., Rodrigues, R. L., Ramos, J. L. C., de Souza, F., & d. F. (2018). Effects of learning analytics on students\u2019 self-regulated learning in flipped classroom. International Journal of Information and Communication Technology Education (IJICTE), 14(3), 91\u2013107.","DOI":"10.4018\/IJICTE.2018070108"},{"key":"9650_CR39","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.compedu.2015.08.004","volume":"89","author":"B Tabuenca","year":"2015","unstructured":"Tabuenca, B., Kalz, M., Drachsler, H., & Specht, M. (2015). Time will tell: The role of mobile learning analytics in self-regulated learning. Computers & Education, 89, 53\u201374.","journal-title":"Computers & Education"},{"key":"9650_CR40","unstructured":"Verma, S., Dickerson, J., & Hines, K. (2020). Counterfactual explanations for machine learning: A review. arXiv preprint arXiv:2010.10596."},{"issue":"3","key":"9650_CR41","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1007\/s10758-021-09580-9","volume":"27","author":"K Vilkova","year":"2022","unstructured":"Vilkova, K. (2022). The promises and pitfalls of self-regulated learning interventions in Moocs. Technology, Knowledge and Learning, 27(3), 689\u2013705.","journal-title":"Technology, Knowledge and Learning"},{"key":"9650_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115222","volume":"182","author":"J Wainer","year":"2021","unstructured":"Wainer, J., & Cawley, G. (2021). Nested cross-validation when selecting classifiers is overzealous for most practical applications. Expert Systems with Applications, 182, 115222.","journal-title":"Expert Systems with Applications"},{"issue":"4\u20135","key":"9650_CR43","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1080\/10447318.2018.1543084","volume":"35","author":"J Wong","year":"2019","unstructured":"Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G.-J., & Paas, F. (2019). Supporting self regulated learning in online learning environments and Moocs: A systematic review. International Journal of Human-Computer Interaction, 35(4\u20135), 356\u2013373.","journal-title":"International Journal of Human-Computer Interaction"},{"key":"9650_CR44","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.iheduc.2015.11.003","volume":"29","author":"JW You","year":"2016","unstructured":"You, J. W. (2016). Identifying significant indicators using lMS data to predict course achievement in online learning. The Internet and Higher Education, 29, 23\u201330.","journal-title":"The Internet and Higher Education"},{"key":"9650_CR45","doi-asserted-by":"crossref","unstructured":"Yousef, A. M. F., & Khatiry, A. R. (2021). Cognitive versus behavioral learning analytics dashboards for supporting learner\u2019s awareness, reflection, and learning process. Interactive Learning Environments, 1-17.","DOI":"10.1080\/10494820.2021.2009881"},{"issue":"1","key":"9650_CR46","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1207\/s15326985ep2501_2","volume":"25","author":"BJ Zimmerman","year":"1990","unstructured":"Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25(1), 3\u201317.","journal-title":"Educational Psychologist"},{"issue":"2","key":"9650_CR47","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1207\/s15430421tip4102_2","volume":"41","author":"BJ Zimmerman","year":"2002","unstructured":"Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into practice, 41(2), 64\u201370.","journal-title":"Theory into practice"},{"key":"9650_CR48","doi-asserted-by":"crossref","unstructured":"Zimmerman, B. J., & Campillo, M. (2003). Motivating self-regulated problem solvers. The psychology of problem solving, p. 233262.","DOI":"10.1017\/CBO9780511615771.009"},{"key":"9650_CR49","unstructured":"Zimmerman, B. J., & Schunk, D. H. (2011). Handbook of self-regulation of learning and performance. Routledge\/Taylor & Francis Group."}],"container-title":["Technology, Knowledge and Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10758-023-09650-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10758-023-09650-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10758-023-09650-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T22:02:29Z","timestamp":1707602549000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10758-023-09650-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,25]]},"references-count":49,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["9650"],"URL":"https:\/\/doi.org\/10.1007\/s10758-023-09650-0","relation":{},"ISSN":["2211-1662","2211-1670"],"issn-type":[{"value":"2211-1662","type":"print"},{"value":"2211-1670","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,25]]},"assertion":[{"value":"15 April 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}